Short-term microgrid load probability density forecasting method based on k-means-deep learning quantile regression

Traditional short-term load forecasting (STLF) methods for large utility grid systems usually provide the forecasted load with deterministic points. However, deterministic load forecasting cannot reveal the load pattern and uncertainty of controllable load in a microgrid, where the prediction errors may exceed the expected range due to the high volatility and strong randomness. In order to deal with this matter, a probability density forecasting method is proposed to predict the microgrid load with uncertainty for robust power scheduling in this paper. The proposed probability forecasting method effectively combines several datadriven and statistical algorithms, including the k-means algorithm, quantile regression long short-term memory neural network (QRLSTM), and kernel density estimation (KDE). Firstly, similar days related to the prediction day are selected through the k-means algorithm, and the historical load data of these selected days are divided into two subsets including the training dataset and the testing dataset. Secondly, a QRLSTM-based model is established and used to predict the microgrid load for different quantiles. Finally, the probability density function of the predicted points is obtained by KDE on the target day. The prediction accuracy is evaluated roundly and the results demonstrate that the proposed method can effectively reproduce the probability density distribution of the load and provide noticeably better performance than some benchmark methods. (C) 2022 The Author(s). Published by Elsevier Ltd.

Automated summary

In this paper, a probability density forecasting method is proposed to predict the load in a microgrid with uncertainty. The method effectively combines multiple algorithms to improve the accuracy and performance of load forecasting, especially in handling the uncertainty and volatility of controllable load.